This work addresses the control design problem for Cyber-Physical Systems under the presence of denial-of-service (DoS) attacks. The system under attack is modeled as a switching system constructed following the limitation of the malicious agent of launching continuous DoS attacks. Different actions are proposed to mitigate the effects of the attack on the system. To illustrate the efficacy of the method in stabilizing the system under the existence of DoS attacks, a numerical experiment is presented.
Labour in most agro-industrial plants accounts for a significant part of the cost structure. Even though fully automated processing facilities can be built, a lower initial cost solution involves training the personnel to increase their efficiency in the plant. Advances in VR technologies enables the creation of immersive virtual environments that simulates different contexts. A VR experience is proposed in order to simulate the work environment for fruit processing line personnel. In this manner, the workers can be trained without using the physical resources of the processing plant. A usability test was performed, showing that participants felt a high degree of immersion and realism. They were able to complete the assigned tasks, indicating that the software meets the objective of instructing the participants in the selection process of IQF berries.
The main complaint of people who start in the automation area is usually that what is seen in an educational classroom is usually far from reality, it starts with simulations, and some educational units have little material and prefer not to risk it to make modifications that may damage the cell or control equipment. In this work, a low-cost manufacturing cell is taken, engineering is carried out such as electrical and pneumatic plans, the arrangement of the cell is reorganized, various components are added or replaced to make the most similar to what that might be found in an industrial area.
The robotic mobile system Robotino, developed by FESTO, is a robotic platform oriented towards research and education, which is why it has a reduced amount of actuators of limited real functionality. This article proposes the integration of a Raspberry Pi 4 to a Robotino FESTO with the purpose of expanding the number of digital inputs this robot has, in addition to allow the input of analog signals by serving as a communication interface between devices through an ethernet connection. In this way, the installation of robotic arm Lynxmotion AL5 with 5 degrees of freedom to the Robotino through the Raspberry Pi 4 is proposed so that it performs additional functions and applications without interfering with the digital inputs of the Robotino, that are already used by the sensors and accessories installed by default. By establishing the connection between both devices (Robotino and Lynxmotion AL5 robotic arm),a mobile system of 7 degrees of freedom of great versatility will be obtained. Results validation was made with the Robotino View simulation software and through real tests.
In 2023, the partners of the ITAIPU Binational Entity, Paraguay and Brazil, will have to review Annex C of the ITAIPU Treaty, which relates to the conditions of the tariff structure and commercialization of the energy produced by the power plant. The governments must define strategies for the negotiation based on the diversity of interests of the different segments of society, particularly in Paraguay, where the debate on these negotiations extends to society in general. Given the location and importance of ITAIPU for the region, the negotiation to be held should devote considerable effort to the revision of Annex C of ITAIPU. This article evaluates the implications of the components of the Cost of Electricity Service (CSE) in terms of what Paraguayan society considers important, using a multiple criteria type methodology called Perceptor Hierarchical Decision (PHD). For this purpose, surveys were carried out to know what the respondents want according to their needs. We evaluated the influence of the CSE components on the fulfillment of the PND 2030, taking into account the preferences of society. The results show that the component that relates to Energy Assignment is the one that could help to achieve greater objectives of the National Development Plan (PND) 2030. These results suggest that this component should be taken into account when preparing the negotiation strategy, taking into account the considerations.
The current computational advance allows the development of technological solutions to everyday problems, for example, today mobile robots are used for the automated transport of raw materials. Therefore, the study of applications such as obstacle avoidance and autonomous navigation with mobile robots are of great importance in academia, driving research that seeks to contribute to scalable and low-cost solutions with mobile robots. Next, a proposal to use artificial vision and add classification functionalities to the navigation control of a mobile robot is described. The solution consists of integrating an embedded system into the mobile robot’s navigation control which, through a classification model, identifies the closest traffic signal and then sends a command to the mobile robot so that it modifies its navigation. This work seeks to verify the technical feasibility of using electronic development boards to develop Machine Learning solutions, by obtaining a classifier with an accuracy of 94.9% and a loss rate of 0.21.
Magnetic levitation is a complex, highly nonlinear, and absolute unstable system. The setback of this process for making educative platforms is the need for real-time position sensing of the suspended ball. This paper describes the levitation of a metallic object by a state feedback control loop when the only measured output is the electrical current. The process is detailed in stages, starting with the physical equations that describe this system, the linearization of the model, and showing that the system with the electric current as output is controllable and observable. Subsequently, the authors show the feasibility of applying quadratic optimal, discrete-time controller by simulation. The practical advantages of this design are the use of sensors for online measurement of electrical current instead of the ball’s position, showing that assigning the current as a controlled variable is a viable alternative to building educational platforms for the magnetic levitation system.
This paper presents the implementation of a multimodular DC-AC converter with fault-tolerant coupled predictive current control. The proposed approach is suitable for applications that require high power transfer with lower size and volume, and it also offers more availability due to the fault-tolerant capacity. Simulation results are presented to validate the proposed control scheme.
The use of artificial intelligence techniques for the recognition of human activity has been an important area of research. Several approaches have been proposed and a large part of them address this problem through vision with conventional RGB cameras. Some of the most significant problems in human activity recognition systems are privacy, the limitations of the operating devices and the comparison of machine learning and deep learning techniques for the prediction of said activity. The present document presents an activity recognition system by means of a recurrent neural network BERT, based on camera vision, using image sequences containing the information of the detection of the pose of the human skeleton for the extraction of characteristics. The proposed method is evaluated with the UP-Fall data-set, outperforming the results of other activity recognition systems that use deep learning techniques and the same data-set. This method achieves an accuracy of 99.14% and a F1-Score of 80.95%.
The purpose of this research work is to develop a system to correct the noise present in images, using computer vision and automatic learning tools, specifically, focused on processing images used by assisted driving systems, in which the presence of noise represents a loss of information, directly affecting the efficiency of such systems.To achieve the above, a methodological process of analysis, design, and evaluation was followed, thus obtaining a neural network model capable of responding to the proposed task, which was verified by performing tests and applying metrics such as PSNR and SSIM, observing that the system can increase up to 14 dB the PSNR value and approximately 0.76 the SSIM value concerning the initial one, this specifically for the case of the tests with Gaussian noise, while for the Salt & Pepper noise the PSNR value increased by 8.53 dB and approximately 0.73 the SSIM value concerning the initial one.
Biometric identification systems play an essential role in multiple application areas, such as banking services, e-government, and public security, among others. Particularly, palm vein recognition is considered an emerging technology from the last decade, avoiding forgery possibilities and presenting high identification reliability and accuracy. State-of-the-art in palm vein recognition has improved its results in recent years from different approaches based on deep learning. Some methods based on convolutional neural networks reported in the literature have achieved high recognition rates in public databases. However, computational simplicity and generalization capability are limited given the small number of samples in the databases. This paper introduces a model called PVEIN-MLELM based on the Multilayer Extreme Learning Machine (ML-ELM) for identifying persons through palm vein images. The ML-ELM algorithm offers advantages in terms of computational simplicity and speed of the training process while maintaining its generalization capability. Experimental results on four public datasets show recognition rates comparable to the state-of-the-art approaches while reducing memory requirements and significantly speeding up computational time.
Nowadays, the growing increase in energy demand worldwide intensifies the search for clean and renewable energy sources used and, among the current technologies, wind energy has been highlighted like one of the most promising. This work presents a new proposal for a wind turbine conversion system using a Frequency Electromagnetic Regulator (FER) that replace gearbox functionalities and improve mechanical transmission system performance due to the high reliability and reduction of mechanical losses and maintenance costs. Besides, FER can be useful in several transmission systems wherein mechanical output speed control is the goal. The modeling is based on the wind power extraction rules, conventional three-phase symmetrical and balanced induction machine, kinematic coupling and motion equations and its performance are validated using simulation results that employ Finite Element Method (FEM) to deal with differential equations approximations.
Microgrids are an important topic in the energy systems of the future. Three of the goals of the energy systems are the decentralization, to decarbonize, and democratize. The investigation in this topic has gone too far in many aspects, but there still things the research doesn't count. For expample, the differences in grid profile in different countries, in terms of frequency and voltage of the utility grid. Other issue is that most of the research its done in english, leaving behind a group of learners that wants to enter in the topics of microgrids. In this paper, a three-phase highly resistive low-voltage microgrid is used in which an inductive virtual impedance is implemented, with the aim of observing its behavior within the determinated conditions, like chilean grid profile and a high R/X ratio.
Extreme Learning Machine (ELM) is a neural network training paradigm that is characterized by simplicity, speed and high level of accuracy. The tuning of the network parameters is normally carried out with non-linear optimization algorithms that break this principle of simplicity and reduced execution time. This article shows that ELM network tuning can be performed efficiently by simple optimization algorithms, consistent with its basic philosophy. Experiments with 8 optimization algorithms are shown, considering 6 widely used databases in training algorithm benchmarks. The numerical results show that the Golden Section Algorithm dramatically reduces the network hyperparameter search time compared to the search while maintaining a high level of accuracy.
Modular Multilevel Converters have been proposed for high-voltage direct current and Low-Frequency Alternating Current transmission systems according to benefits associated with their topologies, such as scalability, modularity, high voltage and power capacity. In industrial applications, hundreds of power cells have to be connected in a cascade to reach high-power ratios. However, most of the academic prototypes are equipped with a low number of power cells to validate academic research. Moreover, they are limited to conventional frequency operation in AC ports over the range of 50-60Hz. Hence, this paper presents the experimental results of a Modular Multilevel Converter test-bench comprised of sixty power cells and through OPAL-RT cabinets. Partial results validate a power flow regulation with power factor correction for low-frequency operation at 10-20 HZ.
This article proposes the development of an information model that allows starting the digital transformation of a small or medium-sized company (SME) using the OPC UA protocol. The model is made up of a hardware component made up of the process plant and the control equipment, as well as a software component where the information model is implemented.As an application case, the proposed information model is developed in a unit process plant, a filtration plant. The process carried out in this plant is through a filter to remove the solid particles present in the water used in the manufacturing process of a beverage. This plant can be integrated into a beverage and/or soft drink manufacturing system in a small company.The information model is integrated into an OPC UA server, a programmable controller, mapping the process variables such as level, temperature flow, and energy consumption. The information of the technological elements that make up the filtration plant are: serial, manufacturer, and calibration dates: OPC UA clients request information from the OPC UA server and then they are sent through procedures (methods) defined in the server and consumed by the server. client, all this integrates the information model.The foregoing shows the viability of the proposed
Fingerprint classification comes to be a relevant guarantee for efficient as well as accurate fingerprint identification, in particular in the case of dealing with one-to-many fingerprint identification. Nevertheless, owing to massive intraclass variability, insignificant inter-class variability, and perturbations, the current fingerprint classification methods still need to enhance the accuracy without increasing the computational cost. In this paper, we introduce a novel method that combines the best extractor of features reported in the literature (Hong08) with multilayer extreme learning machines to maintain the superior classification capability (more than 90%) by simplifying the training time (feasibility for realization in a commercial firmware).
Decarbonising the energy system requires greater electrification leading to higher power demand and better and more reliable power services. These demands are challenging to meet in weak power grids where significant transmission losses are present, and the system is not very reliable. Photovoltaic (PV) installations combined with storage can relieve congestion in the industrial distribution system, improve load voltage profiles, and increase availability. If implemented on a large scale, a PV and storage system can directly impact demand management on power transmission, providing other benefits, such as increasing the capacity to absorb many renewable energies. The present paper analyses a real case in the Honduran power system. The case consists of a mining facility that requires a relatively large amount of power consumed at a constant rate, i.e., 24/7. Due to frequent blackouts, the facility is often powered by a privately owned fossil fuel-based plant. In this regard, this paper analyses the effect of a PV and storage system when it is used to substitute the fossil fuel-based power plant. The simulation results show more than 80% reduction in the distribution lines losses and an improvement in the voltage stability. Consequently, the results showed that storage could be a potential solution to response demand management and improve the electrical system performance.
This work presents the development of a solution for the detection of the position of glass plates and sheets, through computer vision and image processing techniques. This position information is intended to be used in Computer Numeric Control (CNC) waterjet cutting machines, so that the cutting region can be determined. This solution was built using the Python language, using the computer vision library, OpenCV and the mathematical manipulations library, Numpy. To verify the performance of the proposed solution, a test platform was set up, carrying out experiments with glass samples of different types and colors and, through the tests, a minimum error of 0.02mm was detected in relation to the reference position. Therefore, it is expected through this research to contribute to the automation of the entire process of cutting glass plates and sheets.
There is no single, universal software development methodology that applies to all cases. However, there is one that is better adapted to each type of project, the particularity of each of them requires a detailed arrangement of the activities, whether they are associated with traditional, agile or hybrid methodologies. Considering that small projects and small teams are the ones that mostly lack a methodological framework to guide them, the article presents a methodological tool that helps the project manager of small groups of developers to visualize which methodology to apply. to better face the software project and reduce its risks. Our preliminary results demonstrate the effectiveness of the tool, being well evaluated in the projects involved in the test cases. In addition, it can be affirmed that the need to guide the development methodology was resolved by automating a previously developed framework.
